Analysis of injury severity levels and contributory factors in traffic crashes at signalized intersections under mixed traffic conditions in a low- and middle-income country

Traffic crashes are more likely to occur at intersections due to the complex nature of the traffic movement. This study explored the relationship between the injury severity outcome of intersection-related crashes and the contributing factors such as roadway, environmental, temporal, traffic, and land use characteristics. The analysis was based on five years (2016–2020) of three-legged and four-legged intersection-related crashes in Dar es Salaam, Tanzania. The study used a hybrid approach combining the latent class cluster analysis (LCA) and a logistic regression model in analyzing the injury severity of intersection-related crashes. Three clusters were identified for the three-legged intersection crashes based on traffic volume, lane width, number of lanes, and median type. For four-legged intersections, three clusters were identified based on land use, lane width, and time of the day. A logistic model was developed to identify factors contributing to the injury severity of intersection-related crashes. The results indicated that adverse weather conditions were associated with a lower likelihood of fatal/severe injury for both three-legged and four-legged intersections in the whole dataset and each specific cluster. This study provides an insightful understanding of the effects of these variables on the severity of intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention. Also, the results of this study can help developing nations like Tanzania develop a strategic safety plan focusing on improving safety across all signalized intersections.